Multi object detection method for surface defects of steel arch towers based on YOLOv8-OSRA

doi:10.11996/JG.j.2095-302X.2025061327

Abstract

Abstract:

Steel arch towers, as the primary load-bearing structures of steel arch cable-stayed bridges, require early detection and assessment of their surface defects—such as corrosion, spalling, and cracks—to ensure structural safety. However, traditional manual inspection methods are inefficient, highly subjective, and unable to access concealed areas at high altitudes. To address these challenges, an intelligent detection method based on an improved YOLOv8n-Seg deep-learning framework integrated with the OSRA attention mechanism was proposed. High-resolution internal images of steel arch towers were collected using a self-developed rail-guided inspection robot system. A comprehensive dataset containing 5 846 original images was constructed from both collected and open-source data, and data augmentation techniques—including random cropping, mirroring, and brightness adjustment—expanded the dataset to 23 378 images. At the algorithmic level, the OSRA attention module was innovatively embedded into the feature fusion layer of the YOLOv8n-Seg network. By leveraging an overlapping patching strategy and a local refinement mechanism, the model’s ability to capture irregular boundaries and small-scale defect features was significantly enhanced. Experimental results demonstrated that the optimized YOLOv8-OSRA model achieved notable performance improvements on an independent test set: corrosion detection mAP@0.5 reached 90.9% (+2.6%), crack identification accuracy reached 87.0% (+1.1%), and spalling detection accuracy reached 81.9% (+2.1%). Ablation experiments further confirmed that the OSRA module, while maintaining computational efficiency (increasing GFLOPs by only 0.8%), outperformed conventional attention mechanisms such as SE and CBAM. The findings provided a lightweight and deployable solution for steel arch tower defect detection, and the proposed multi-scale feature enhancement approach offered valuable insights for detecting surface defects in complex steel structures.

Key words: bridge engineering, wheel rail inspection robot, surface defect detection, computer vision, deep learning, YOLOv8

CLC Number:

WANG Haihan. Multi object detection method for surface defects of steel arch towers based on YOLOv8-OSRA[J]. Journal of Graphics, 2025, 46(6): 1327-1336.

Figures/Tables 16

Fig. 1 Architecture of the C2F module

Fig. 2 YOLOv8-Seg network architecture

Fig. 3 Real bridge inspection ((a) Steel arch tower cable-stayed bridge; (b) Track-mounted inspection robot)

Fig. 4 Typical damage types ((a) Rust; (b) Apparent cracking; (c) Coating shedding)

Fig. 5 Data annotation ((a) Rust; (b) Crack; (c) Coating peeling)

Fig. 6 Data enhancement of typical damaged images ((a) Original image; (b) Mirror image; (c) Brightness adjustment; (d) Cropping)

Fig. 7 Network architecture of the YOLOv8-OSRA model

Fig. 8 Loss value change curve ((a) Loss value change curve of bounding box; (b) Loss value change curve of segmentation)

Table 1 Comparison of training results of different networks/%

类别	边界框		分割掩码
类别	YOLOv8n-Seg	YOLOv8-OSRA	YOLOv8n-Seg	YOLOv8-OSRA
锈蚀	88.3	90.9	77.8	81.5
表观裂缝	85.9	87.0	63.3	65.5
涂层脱落	79.8	81.9	65.6	67.3
mAP@0.5	84.6	86.6	68.9	71.4

Fig. 9 PR curve of YOLOv8n-Seg ((a) Bounding box; (b) Mask)

Fig. 10 PR curve of YOLOv8-OSRA ((a) Bounding box; (b) Mask)

Table 2 Comparison of computational complexity

模型	GFLOPs	参数量/M	FPS (RTX3050)
YOLOv8n-Seg	12.0	6.5	58.8
YOLOv8-OSRA	12.1	10.2	51.5

Table 3 Comparison of ablation experiments on attention mechanisms

注意力类别	mAP@0.5 (锈蚀)/%		mAP@0.5 (裂缝)/%		mAP@0.5 (剥落)/%		GFLOPs
注意力类别	边界框	分割掩码	边界框	分割掩码	边界框	分割掩码	GFLOPs
无	88.3	77.8	85.9	63.3	79.8	65.6	12.0
SE	80.1	66.8	81.7	57.7	76.0	61.0	27.6
CBAM	80.9	65.8	82.7	57.6	76.5	62.3	27.7
SRA	78.9	64.6	81.6	57.7	76.0	62.3	27.8
OSRA	90.9	81.5	87.0	65.5	81.9	67.3	12.1

Table 4 Comparison of mainstream networks

模型	mAP@0.5/%		FPS	Params/M	GFLOPs
模型	检测框	分割掩码	FPS	Params/M	GFLOPs
YOLOv5	73.8	58.0	59.5	5.6	11.0
YOLOv7	81.6	66.5	58.8	13.6	47.7
YOLOv8 (基线)	84.6	68.9	58.8	6.5	12.0
YOLOv8-OSRA	86.6	71.4	51.5	10.2	12.1

Fig. 11 Comparison of detection results before and after model optimization ((a) Original image; (b) Detection result of YOLOv8n-Seg model before optimization; (c) Detection result of YOLOv8-OSRA model after optimization)

Fig. 12 Operation display window ((a) Data display window; (b) Disease information and location)

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